Systems for monitoring sensor and actuator health and performance

ABSTRACT

Systems for assessing health and performance of actuators and sensors in process equipment system. In one example, the equipment system comprises subsystems (preferably physically coupled subsystems), at least some of which are characterizable by transmitted signals. Some of these signals are transformed into a comparable form and compared, so as to identify signals that are outside of operating bounds.

BACKGROUND AND SUMMARY OF THE INVENTION

The following applications filed concurrently herewith are notnecessarily related to the present application, but are incorporated byreference herein in their entirety: “Methods for Managing Flow ControlValves in Process Systems” (U.S. patent application Ser. No. ______,filed simultaneously with the effective filing date of the presentapplication, Attorney Docket Number HES-2005-IP-019666U3); “Systems forManaging Flow Control Valves in Process Systems” (U.S. patentapplication Ser. No. ______, filed simultaneously with the effectivefiling date of the present application, Attorney Docket NumberHES-2005-IP-019666U4); and “Methods to Monitor System Sensor andActuator Health and Performance”, (U.S. patent application Ser. No.______, filed simultaneously with the effective filing date of thepresent application, Attorney Docket Number HES-2006-IP-019666U1).

The present application relates to monitoring complex systems such asautomated equipment used in oilfields, and more particularly tomonitoring sensor and actuator health and performance.

DESCRIPTION OF BACKGROUND ART

Modern oilfield rigs use automated equipment in many aspects of anoperation. A key element of such complex systems is the control andmonitoring system. These systems include sensors and other elements thatsignal a control unit in a feedback loop. The control unit monitors thesystem, providing stability and ensuring the system operates withindesired parameters.

Sensors are often placed at specific locations within a system toprovide information necessary for the control unit to function. Forexample, on a drill rig, mud must be provided within specificparameters. Sensors monitor the flow rate of the mud, pressure, density,and other measurables, and this information is fed back to the controlunit and/or to an operator who manually monitors the system forfailures.

Current systems normally rely on operators to take action when failureoccurs. These failures can affect job performance and lead to jobfailure. Also, the operators receive minimal feedback from the controlsystem about its current operating state relative to its expected state.This means an operator is liable to be unaware of impending or immediatefailures, and requires a higher degree of knowledge on the part of anoperator. The lack of diagnostic systems to monitor performance and aninterface designed to give an operator assistance means that operatorsare required to have a higher level of skill and knowledge to safely andefficiently monitor and operate these systems.

Systems for Monitoring Sensor and Actuator Health and Performance

In one example embodiment, the present innovations provide a system tomonitor for failures in one or more subsystems (preferably physicallycoupled subsystems) in a larger system, and (in some embodiments) updatethe operator of failures or impending failures to improve processcontrol. It also can include a system with process control knowledge tohelp operation of the equipment and reduce operator error.

In one class of preferred embodiments, the innovations include aplurality of subsystems (such as sensors or actuators, or combinationsof parts) that can signal operation or state information. Thisinformation is used to determine if one or more subsystems are in ornear failure mode.

For example, in one example implementation, a sensor of interest isselected, such as a flow rate sensor. Other subsystems of the totalsystem that are physically coupled to the flow rate sensor provideinformation that is transformed into data that is comparable to theoutput of the flow rate sensor. This information is compared, anddiscrepancies indicate that some sensor of the system may be failing oroutside preferred operating conditions. Operating conditions or boundscan be chosen or generated in a number of ways, including static,dynamic, or operationally dependent bounds. Bounds may be also bereevaluated in real time, in dependence, for example, on systemdynamics.

In another example implementation, subsystem signals are aggregated andtransformed into comparable form so that discrepancies can beidentified. Thus, for example, multiple physically coupled subsystemsform a redundant check on one another so as to monitor each individualsubsystem's health and performance.

In preferred embodiments, actual subsystem (e.g., sensor or actuator)readings are compared to a model of the system dynamics, so actualsubsystem operation can be compared to expected subsystem operation.

By using the available sensor data in conjunction with a model of thesystem dynamics, the controller can be designed to estimate sensor andactuator failures and update the operator through the interface. Thecontroller can also be designed with system intelligence which can beused to help the operator perform the job and reduce operator error.

The disclosed innovations, in various embodiments, provide one or moreof at least the following advantages:

detection of individual sensor or actuator failure or inaccuracy;

overall system health monitoring;

reduction of necessary operator skill and chance of operator error;

ability to switch control modes depending on sensor or actuator health.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed inventions will be described with reference to theaccompanying drawings, which show important sample embodiments of theinvention and which are incorporated in the specification hereof byreference, wherein:

FIG. 1 shows one embodiment of the present innovations as implemented inan exemplary hydrocarbon well drilling rig site.

FIG. 2 shows an example of actuator slippage.

FIG. 3 shows a sand and liquid slurry system consistent withimplementing an embodiment of the present innovations.

FIG. 4 shows a detail of the liquid supply side of the sand and liquidslurry system consistent with implementing an embodiment of the presentinnovations.

FIG. 5 shows a control diagram of a blender unit consistent with anembodiment of the present innovations.

FIG. 6 shows an example implementation of redundant sensor checkingrelative to dynamic links of a physical system, consistent with anembodiment of the present innovations.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The numerous innovative teachings of the present application will bedescribed with particular reference to the presently preferredembodiment (by way of example, and not of limitation).

FIG. 1 shows an example system in which embodiments of the presentinnovations can be implemented. This example shows an oilfield drillingsystem 100, including a drill string 102, and downhole tool 104.Drilling system 100 also includes a pump system 106 which controlsinsertion of materials downhole, such as drilling mud for cooling andremoval of debris, or other slurries (such as sand and watercombinations) for various tasks.

In a preferred embodiment, the drilling system 100 includes sensors suchas flow meter 101 that monitor and characterize the performance ofvarious subsystems. This information is used, often by an operator, butalso by automated systems, to determine when performance is outsidedesired bounds or failure occurs or is about to occur.

Specifically, FIG. 1 also shows one embodiment of the presentinnovations as an oilfield equipment system 100 which can be comprisedof a pump system 106, a rotary flow control valve with anactuator/position indicator assembly as 103, a flow meter 101, a drillstring 102, a drill bit down hole at 104, and a plurality of signaloperations, computations, and other actions that can be configured witha general purpose computer (not shown) that is monitoring system 100.Pump 106 can pump a drilling fluid through control valve 103 and throughflow meter 101, then down drill string 102 through bit 104 and then canre-circulate the fluid back to itself. Thus, the pump, the valve, andthe meter are physically coupled by the drilling fluid. Pump 106 cansend a pump speed signal to stage 106A for transformation of the speedsignal to a volumetric fluid flow rate, in say, gallons per minute(“GPM”). Flow meter 101 can send a flow rate signal to stage 101A fortransformation to a volumetric fluid flow rate in GPM. Valve 103position indicator can send a signal to stage 103A for transformation ofthe “% OPEN” signal of the valve to a volumetric flow rate in GPM. Stage107 can compare the three transformed signals for agreement in stages107A, 107B, and 107C. If one signal is found to disagree with the othertwo signals, an output signal can be made to notify an operator that theparticular component that is not in agreement needs maintenance orattention. Further, the output signal can be used to effect an automaticreconfiguration of the control system operating the overall system 100to thereby exclude the disagreeing signal from the control methods beingused to operate the system.

For an example of a rotary-actuated valve, FIG. 2 shows a top view of anexample rotary-actuated valve 206 that is operated by an actuatorattached to the valve shaft 208, which opens and closes the valve byrotating the valve shaft according to a signal. In some situations, suchas when a valve is stuck, aged, or otherwise not operating correctly,there can be a difference between the signaled valve movement 202 andthe actual valve movement 204. In the example of FIG. 2, the actuatorwas signaled to move the valve a first amount 202, while the actualvalve movement 204 was less. For example, the difference in movement canrepresent a difference in the signaled angle of rotation. In otherinstances, a valve can be vertically actuated and the difference canrepresent the error in valve stroke. In some situations, reports ofvalve movement can depend on signaled movement 202 and not actualmovement 204. Especially in complex systems, failure to obtain accurateinformation about actual subsystem performance (such as the movement ofthe valve) can harm production and propagate to other parts of thesystem.

In one example embodiment of the present innovations, subsystems of alarger system (preferably physically coupled subsystems, or subsystemsthat can otherwise be characterized in terms of one another) areredundantly monitored. For example, subsystems that affect a sensor oractuator (in preferred embodiments) are compared in order tocharacterize a given sensor or actuator's current, actual level ofperformance in order to determine if the sensor or actuator isperforming within accepted bounds.

Inputs and outputs that affect (or are affected by) the subsystem are,in preferred embodiments, transformed into comparable sensor or actuatorstates to monitor sensor or actuator performance. For example, when agiven system includes several sensors that monitor physically coupledsubsystems, some or all the sensors outputs can be transformed into thesame units or data as one of the sensors, to determine if that sensor issending accurate signals of the subsystem which it monitors. Bytransforming these signals into a single, comparable set of data, thepresent innovations provide a way to redundantly check each individualsensor of the group of sensors. This redundant checking can be performedin a number of ways, such as by selecting a sensor of interest andtransforming all other sensor data into data that is comparable to thesensor of interest, or by transforming all sensor data into a singleform so their signals can be aggregated and compared, for example, bychecking standard deviations between signals, spread, and otherstatistical analysis.

For example, a sensor or actuator of interest can be viewed as beingcoupled (such as physically coupled) to other actuators and sensors ifthe signal or operation of one is affected by, or affects, the otheractuators or sensors. Transformation of the various signals is derivedfrom physical system dynamics. The transformed signals of multiplecoupled subsystems effectively become redundant sensors.

In preferred embodiments, subsystem performance, as determined by one ormore of the redundant sensors, is compared to predetermined or dynamicbounds to determine if the subsystem is performing properly, forexample, or close to or in failure. These bounds can be static oroperationally dependent, and/or reevaluated in real time. Otherperformance constraints can be created from the dynamic limits of thephysical system. The physical system operational envelop can be defined,for example, as a state vector of first order derivatives (i.e., changeover time) which can be used to define acceptable operational ranges ofthe sensors. Such a mechanism can be used to detect, for example, when asensor registers severe change, which can indicate either a subsystem infailure, or sensor malfunction. Operational bounds or envelopes can alsobe dynamically reset, for example, relative to physical system dynamics.

Further embodiments of the present innovations include interfaceswherein results of one or more of the redundant sensors are reported toan operator, preferably coupled with information to help the operator orgive assistance in detecting, for example, when corrective action needsto be taken and reduce operator error.

In many complex systems, such as those described below, sensorinformation is used in feedback loops to aid in controlling systems toprovide stability and to ensure that a system operates within acceptablelimits or bounds. When data from a plurality of sensors are used by acontrol unit in a feedback and control system, the present innovationsallow for more robust control in several ways. For example, in oneexample embodiment, if a plurality of sensors are used to inform acontrol unit, and if one of those sensors goes out of operationalbounds, that sensor's signal can be removed from input to the controlunit. In preferred embodiments, the control algorithm used in thecontrol system can be modified to operate without the data from thesensor that was removed. In other embodiments, a sensor can experiencetemporary periods when its signal is outside of operational bounds,indicating bad sensor data, for example. In such cases, the sensor canbe temporarily removed from input to the control unit, and later, whenit has resumed operation that is within operational bounds, its signalcan be reintroduced to the control unit.

The present innovations are discussed with reference to an examplesystem, such as that depicted in FIG. 3. In this case, a sand and liquidblending system 300 that includes a sand supply 302, a liquid supply304, a blender 306, and a pump system 308. In this example, because ofsuch physical realities as fluid dynamics, various parts of the systemare physically coupled. For example, the input and output of the blenderare dependent on one another, in that changes in one are affected by,affect, or can otherwise be detected in changes in the other. Forexample, measured rate of flow into the blender would be coupled withmeasured rate of flow out of the blender. These two quantities couldtherefore be expressed as functions of one another. More detailedexamples follow.

FIG. 4 shows a detailed view of the liquid supply subsystem 400 of thesystem shown generally in FIG. 3. Liquid supply tank 304 sends liquid toblender 306 which outputs to a pump system 308. Output from liquidsupply tank 304 is monitored by a flow sensor 402 and is controlled by avalve 404. Downstream of blender 306, another flow sensor 406 monitorsoutput to the pumping system 308.

Because, in this example, all these elements are physically coupled (viathe flow stream hydraulics, in this example), they can be characterizedin terms of one another. For example, flow sensor 402 directly measuresthe liquid flow rate. However, changes in the height of the liquidsupply tank 304 over time and the area of the tank can provide anexpression that also provides a determination of flow rate that iscomparable to, or should agree with, that directly measured by sensor402. Likewise, valve 404 can be used to express rate as a function ofthe valve flow constant, the valve-open angle and drive signal appliedto the valve 404. The blender 306 and flow sensor 406 can, together,provide rate as a function of the height, the change in height overtime, the area, density, and output flow of the blender. Finally, ratecan be expressed at the pumping system 308 in terms of the efficiency,output curve, and RPMs of the pumping system.

These multiple functions that result in flow rate determinationseffectively form a system or plurality of redundant sensor measurementsfor flow rate measurements (in this example). In one embodiment of thepresent innovations, these values are compared to the sensor 402 todetermine if the sensor 402 is operating correctly. For example, if thesubsystems that also indirectly measure the flow rate yield a relativelyconsistent flow rate, and if sensor 402 differs significantly from thisrate, then the accuracy of sensor 402 is called into question. In otherembodiments, all five of these subsystems (including sensor 402) can beaggregated and statistically analyzed, for example, by measuring theirstandard deviation, and/or identifying any individual subsystem thatdiffers from the other readings beyond a predetermined threshold orenvelope. Other statistical manipulation or analysis of these data isalso possible.

Thus, the various data of the subsystems can be dynamically transformedinto an interested subsystem's performance.

The disclosed sensor checking and dynamic characterization system can beused in other ways as well. For example, in one embodiment, if a sensoris found to operate outside of predetermined (or dynamic, oroperationally dependent) bounds, that sensor can be removed. In otherembodiments, the sensor can be temporarily removed, and reintroducedwhen its operation returns within desired limits. Changes in the sensoroperation over time, as detected by the present innovations, can alsoexceed limits as described above. In other embodiments, a sensor orsubsystem might go out of operational bounds and be removed from inputto the control algorithm that maintains stability in the system. In someembodiments, the sensor's input is simply removed, and may or may not bereintroduced when the sensor is once again found to be operating withindesired limits.

In other embodiments, the sensor's input is removed (temporarily orpermanently) and, additionally, the control algorithm is modified toaccount for the reduced input information. For example, some cementmixing systems can be designed to switch from being controlled usingdensity information (i.e., information from densitysensors/calculations) to being controlled using volume information i.e.,information from volume sensors/calculations). In such an examplesystem, if the density sensor is determined to be in a failing mode andis removed from the input to the control algorithm, then the system canswitch from density mode to volumetric mode, and thereafter the controlalgorithm would be modified to accept and use information gathered fromthe sensors associated with the volumetric mode. Other examples alsoapply, such, such as when a height sensor fails, the innovative systemcan switch to density mode and use the changed input in its controlalgorithm. In these example cases, in preferred embodiments, an operatorwould be informed and may have to take necessary actions, such ascontrolling some levels manually.

FIG. 5 shows a further detail of the blending system 306 shown in FIG.3, showing the control loops that maintain stability in the respectivesystems. A density sensor 502, a height sensor 504, a water sensor 506,and a sand sensor 508 are shown in context of a control system diagram.Each control loop includes a control unit or algorithm, represented byPID (proportional, derivative, integral) controller (shown variously asunits 502A-508A) that is associated with elements in the forward path,between the error signal and the control signal. (Other types of controlmodels can of course be implemented, and the present example isillustrative only.) The depicted system includes signals that representthe error between the dynamic models (502B-508B) and the outputs oftheir respective sensors. Each sensor measures some property that isalso being dynamically modeled. The input to the dynamic models from thePIDs (in this example) are the amounts needed to correct the dynamicmodels so they match their respective sensor readings. Each control loopalso has a dynamic model (502B-508B) of the system or subsystem on whichthe control unit imposes stability.

As mentioned above, the other inputs and outputs can be dynamicallytransformed into an interested system's performance. In this example,there are three ways to determine expected sand rate. The mass rateerror signal can be dynamically transformed (in the same way thatreadings were transformed into liquid flow rates, above) to achieve anexpected sand rate 502C. Likewise, the volumetric rate error signal canbe transformed into an expected sand rate 504C. And the sand screwdynamic model gives a measure of the sand rate by taking into accountthe drive signal, the speed of the screw, and other known dynamics.

It should also be noted that this system contains an adaptive parametriccontrol (APC) to map nonlinearities. This concept can be applied inseveral ways, such as examining actuator, valve, or other systemperformance and identifying problems.

For example, in one embodiment, the APC is used in examining actuatorperformance and looking for problems.

There are several ways this innovative concept can be implemented, andsome examples follow. These examples are intended to describeembodiments, and not to limit the application of the innovativeconcepts.

In general terms, these innovative concepts include, in a firstembodiment, modeling of the dynamics of a system as expected in normaloperation; modeling the dynamics of the system in real time; andcomparing the two models to determine if a failure has occurred. Inanother embodiment, the present innovations include embodiments that usea learning algorithm to determine a parameter in a model of thedynamics; and using that parameter to detect system failure, such as bymonitoring that parameter (or systems from which that parameter can bederived) during operation.

In a first example, a model of failure behavior is generated. The modelof system failure is compared to the system as the system is running.This comparison can provide additional information, about both thefailure model and the system dynamics. For example, the dynamics ofvalve slop (or mismatch between a valve control signal and actual valveperformance) may be well known. The model of valve slop can be comparedto the system dynamics while the system is running. For example, thedeadband of the valve and the valve coefficient (or an aspect of thecontrol signal) can be mapped so as to increase the accuracy of thevalve slop model. This will provide information about the wear that isoccurring and the flow characteristics through the valve.

In another example, the dynamics of the system are mapped while thesystem is running, but without a model of how the system fails ormisbehaves. In this case, the mapped dynamics are compared to athreshold value, such as one or more dynamic performance specifications,to see if the mapped system dynamics are within bounds. For example, apump's performance can be modeled under normal operating conditions. Theparameters of that model can be dynamically compared to actualperformance while the system is running. The system under normaloperating conditions should produce a torque feedback doe to dampingthat is a function of speed. If the mapped damping coefficient becomeslarge, and outside the specs, a problem may have occurred, such as thepump experiencing environmental loading. This could be, for example, asign that the piston chamber is filled with sand. The number of sensorsand observable states would determine how many properties could bemapped to the dynamic model or thresholds.

In another example, a learning algorithm (such as a neural network)determines normal operating behavior. The model created by the learningalgorithm can be compared to sensor data to determine how well thesystem is tracking “normal” behavior, and to thereby detect failures.

These subsystems effectively serve as virtual sensors, and their outputsare input to a sensor analysis program 510, such as a computer programproduct on a computer readable medium that analyzes the readings, asdescribed above. For example, the sensor readings can be monitored forbehavior so as to indicate (for example, by a signal to an operator orby automated alarm or controls) when a given sensor is operating outsidepredetermined bounds (whether dynamic or static).

FIG. 6 shows sensor checking relative to dynamic limits to the physicalsystem. Here, the known operational envelop, shown as lower bound (LB)and upper bound (UB) are used to check the sensor and actuatorperformance relative to the current operating position and derivative ofthat position. The current will determine the allowable sensor envelope.As an example, if a mixing tub is being filled with gel and sand, andthat mixture is leaving the mixing tub at some rate, then the rate ofchange of the tub level sensor should output a signal value that isclose to what would be expected for that rate of change of volume.

FIG. 6 includes a plurality of levels of checking. For example, thewater rate includes three separate levels of performance checks. In afirst case, the water rate is directly measured, for example, by a flowmeter or other means of checking movement of the water. Lower bounds andupper bounds are set for the water rate, and if the water rate exceedsthese bounds, a signal indicating unacceptable behavior or performancecan be sent. A second condition for bounding the water rate is based onthe commands sent to the actuator that controls the water rate. Knownchanges in the actuator correspond to known changes in the water rate.If a given command is sent, and yet the water rate does not respond asexpected (within bounds), then a signal indicating this behavior can besent. Finally, the change in the water rate can be used to set bounds onthe water rate. In this case, the dynamic behavior of the water ratecan, for example, have known bounds outside which unacceptable behavioris indicated. For example, if it is known that the change in water rateshould not exceed d(water rate)/dt, and if checks on the water rateindicate that the dynamic behavior of the water rate exceeds presetbounds, then a signal indicating such condition can be sent.

All these bounds or indications of the water rate can be used, forexample, as checks on the water rate. In some cases, the water rate, orthe water actuator command, or the dynamic changes in the water rate,may be inferred from data from other (coupled) systems. In such cases,the data from the coupled systems is preferably transformed into one ofthe three example measures for acceptable water rate behavior, andcompared to the predetermined bounds.

As seen from the examples, the present innovations include, in at leastone embodiment, a multi-layered solution in which all the sensors andactuators are combined with system intelligence to determine failure, orlikelihood of failure. (For example, bounds can indicate failure, orconditions that are known or suspected to lead to failure.) Thisprovides an improved view of system health and performance, and alsopermits signaling to operators so that failures are prevented or caughtmore quickly, reducing operator error.

According to a disclosed class of innovative embodiments, there isprovided at least three oilfield equipment subsystems that arephysically coupled to one another, a control system configured toreceive signals from at least some of the oilfield equipment subsystems,and wherein the control system is configured to: transform one or moreof the oilfield equipment subsystem signals into units associated withthe type of physical coupling among the three or more oilfield equipmentsubsystems, compare at least some of the signals and indicate at leastone oilfield equipment subsystem's signal that does not agree with atleast two other oilfield equipment subsystems' signals.

According to a disclosed class of innovative embodiments, there isprovided a An oilfield equipment monitoring system, comprising at leastthree oilfield equipment subsystems that are physically coupled to oneanother, a control system configured to receive signals from at leastsome of the oilfield equipment subsystems, and wherein the controlsystem is configured to check the respective readings of said multiplesubsystems against each other to determine whether any subsystems havereadings which are physically inconsistent with each other; and under atleast some conditions, exclude the output of a respective subsystemwhich has been determined in said check of respective readings to beshowing inconsistent output.

According to a disclosed class of innovative embodiments, there isprovided an oilfield equipment monitoring system, comprising at leastthree oilfield equipment subsystems that are physically coupled to oneanother, a control system is configured to monitor one or more signalsderived from the oilfield equipment subsystems and wherein signals fromdifferent oilfield equipment subsystems are compared to identify anoilfield equipment subsystem's signal that does not substantially agreewith at least two other oilfield equipment subsystems' signals.

Modifications and Variations

As will be recognized by those skilled in the art, the innovativeconcepts described in the present application can be modified and variedover a tremendous range of applications, and accordingly the scope ofpatented subject matter is not limited by any of the specific exemplaryteachings given.

For example, the disclosed innovations can be applied in a number ofareas outside the oil industry, though the preferred context is the oilindustry.

For another example, though many of the examples used to describe thepresent innovations use specific components, such as sensors and/oractuators, the present innovations can be applied using other componentsas well. For example, any detection and signaling apparatus thatreceives information about a system and that can in any way convey thatinformation could be implemented into the present innovations. Theparameters that are monitored can also vary widely, including density,flow, volume, various derivatives, mass transfer, temperature, pressure,and any other characterizable parameter.

For another example, though the present innovations are described in thecontext of a sand and liquid slurry, this is only an example context.Other contexts would also benefit from the present innovations, wherepreferably physically coupled subsystems can be characterized in acommon way.

In another example, the present innovations are only one part of amulti-level filtering system, that can include other checks on systembehavior.

In other examples, the systems being monitored are characterized asbeing “physically coupled,” or “coupled.” Any transfer of information,matter and/or energy between two systems is included in the definitionof “coupled” as that term is used in this application. Further, any twosystems that can be characterized in terms of one another, are alsoconsidered to be “coupled” within the context of this application.

In another example, the current innovations are characterized in thecontext of oilfield equipment. Such equipment includes a variety ofoilfield supply systems, downhole tools, above-ground equipment, such asvalves, screws, pumps, agitators, and other tools associated withoilfield operations.

In another example, the signals associated with the oilfield equipmentsubsystems are described as being transformed into “units” associatedwith the physical coupling that exists among the subsystems. These unitsare understood to include not only physical units (such as mass, volume,rates, or other physical quantities or one or more derivatives orquantities thereof), but also “unitless” mathematical quantities orexpressions which are consistent with or associated with the physicalcoupling (i.e., are derivable from the type of physical coupling) in anyway. For example, the units or expressions into which signals aretransformed for comparison could include normalized quantities where“physical” units have been divided out of the expression. These unitscan also be monotonic expressions of one another, or another quantity.The units or form of the compared quantities are intended to betransformed such that they can be compared with one another, regardlessof the form of the expression.

In another description of the exemplary embodiments, signals associatedwith the various subsystems can refer to, for example, a sensor reading,a control signal sent to a subsystem, a meter or other device that isaffected by the physical coupling of the subsystem that can bemonitored, or any other quantity associated with that subsystem that canbe monitored in some way, and which can be expressed in terms that arecomparable to at least one other subsystem that is physically coupledwith the first subsystem.

None of the description in the present application should be read asimplying that any particular element, step, or function is an essentialelement which must be included in the claim scope: THE SCOPE OF PATENTEDSUBJECT MATTER IS DEFINED ONLY BY THE ALLOWED CLAIMS. Moreover, none ofthese claims are intended to invoke paragraph six of 35 USC section 112unless the exact words “means for” are followed by a participle.

The claims as filed are intended to be as comprehensive as possible, andNO subject matter is intentionally relinquished, dedicated, orabandoned.

1. An oilfield equipment monitoring system, comprising: at least threeoilfield equipment subsystems that are physically coupled to oneanother; a control system configured to receive signals from at leastsome of the oilfield equipment subsystems; and wherein the controlsystem is configured to: (i) transform one or more of the oilfieldequipment subsystem signals into units associated with the type ofphysical coupling among the three or more oilfield equipment subsystems;(ii) compare at least some of the signals; and (iii) indicate at leastone oilfield equipment subsystem's signal that does not agree with atleast two other oilfield equipment subsystems' signals.
 2. The system ofclaim 1, wherein the type of physical coupling is selected from thegroup consisting of: hydrostatic pressure, flow rate, and mass transfer.3. The system of claim 1, wherein the control system is furtherconfigured to modify a control algorithm based on an identified oilfieldequipment subsystem signal.
 4. The system of claim 1, wherein thecontrol system is further configured to send a signal to an operatoridentifying an oilfield equipment subsystem, where that subsystem'ssignal does not agree with at least two other oilfield equipmentsubsystems' signals.
 5. The system of claim 1, wherein the controlsystem is further configured to send a signal to indicate a result isoutside acceptable bounds when the step of comparing indicates theresult is outside acceptable bounds.
 6. The system of claim 5, whereinthe acceptable bounds are selected from the group consisting of:predetermined bounds, dynamical bounds, operationally dependent bounds,and bounds associated with dynamic constraints of a physical system. 7.The system of claim 1, wherein the units are selected from the groupconsisting of: physical units, normalized expressions without physicalunits, and monotonic transformations of physical units.
 8. The system ofclaim 1, wherein the control system is further configured to replace theidentified signal's input to the control algorithm with another signal,without modifying the control algorithm, when the identified signal isan input to a control algorithm.
 9. An oilfield equipment monitoringsystem, comprising: at least three oilfield equipment subsystems thatare physically coupled to one another; a control system configured toreceive signals from at least some of the oilfield equipment subsystems;and wherein the control system is configured to check the respectivereadings of said multiple subsystems against each other to determinewhether any subsystems have readings which are physically inconsistentwith each other; and under at least some conditions, exclude the outputof a respective subsystem which has been determined in said check ofrespective readings to be showing inconsistent output.
 10. The system ofclaim 9, wherein the control system is further configured to include theoutput of a respective subsystem which had been excluded if said checkof respective readings ceases to detect inconsistencies.
 11. The systemof claim 9, wherein the control system is further configured to send asignal to indicate a result is outside acceptable bounds when said checkof respective readings shows the result is outside acceptable bounds.12. The system of claim 11, wherein the acceptable bounds are selectedfrom the group consisting of: predetermined bounds, dynamical bounds,operationally dependent bounds, and bounds associated with dynamicconstraints of a physical system.
 13. The system of claim 9, wherein thecontrol system is further configured to replace a first subsystem'ssignal with a second subsystem's signal as input to a control algorithmif said first subsystem has been determined to be showing inconsistentoutput.
 14. The system of claim 13, wherein the second subsystem'ssignal is transformed into a form comparable to the first subsystem'ssignal before being input into the control algorithm.
 15. The system ofclaim 9, wherein at least one of the subsystems is selected from thegroup consisting of: a sensor, an actuator, a mixer, and a pumpingsystem.
 16. An oilfield equipment monitoring system, comprising: atleast three oilfield equipment subsystems that are physically coupled toone another; a control system configured to monitor one or more signalsderived from the oilfield equipment subsystems; and wherein said signalsfrom different oilfield equipment subsystems are compared to identify anoilfield equipment subsystem's signal that does not substantially agreewith at least two other oilfield equipment subsystems' signals.